Advances in Knowledge Discovery and Data Mining, Part I - neues Buch

EAN (ISBN-13): 9783642136573
Erscheinungsjahr: 2010
Herausgeber: Mohammed J. Zaki#Jeffrey Xu Yu#B. Ravindran#Vikram Pudi

Buch in der Datenbank seit 2016-05-18T23:45:29+02:00 (Vienna)
Detailseite zuletzt geändert am 2023-12-29T08:58:24+01:00 (Vienna)
ISBN/EAN: 9783642136573

ISBN - alternative Schreibweisen:
978-3-642-13657-3
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: clark timothy
Titel des Buches: data mining, knowledge discovery, pacific discovery

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Daten vom Verlag:

Autor/in: Mohammed J. Zaki; Jeffrey Xu Yu; B. Ravindran; Vikram Pudi
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Advances in Knowledge Discovery and Data Mining, Part I - 14th Pacific-Asia Conference, PAKDD 2010, Hyderabat, India, June 21-24, 2010, Proceedings
Verlag: Springer; Springer Berlin
506 Seiten
Erscheinungsjahr: 2010-05-29
Berlin; Heidelberg; DE
Sprache: Englisch
106,99 € (DE)
110,00 € (AT)
124,00 CHF (CH)
Available
506 p. 167 illus.

EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Data Mining; Verstehen; Clustering; bioinformatics; classification; data mining; distributed data mining; fractal representation; graph analysis; knowledge discovery; algorithm analysis and problem complexity; C; Data Mining and Knowledge Discovery; Artificial Intelligence; Information Systems Applications (incl. Internet); Information Storage and Retrieval; Database Management; Algorithm Analysis and Problem Complexity; Data Mining and Knowledge Discovery; Artificial Intelligence; Computer and Information Systems Applications; Information Storage and Retrieval; Database Management; Algorithms; Computer Science; Wissensbasierte Systeme, Expertensysteme; Künstliche Intelligenz; Angewandte Informatik; Informationsrückgewinnung, Information Retrieval; Data Warehousing; Datenbanken; Algorithmen und Datenstrukturen; BC

Keynote Speeches.- Empower People with Knowledge: The Next Frontier for Web Search.- Discovery of Patterns in Global Earth Science Data Using Data Mining.- Game Theoretic Approaches to Knowledge Discovery and Data Mining.- Session 1A. Clustering I.- A Set Correlation Model for Partitional Clustering.- iVAT and aVAT: Enhanced Visual Analysis for Cluster Tendency Assessment.- A Robust Seedless Algorithm for Correlation Clustering.- Integrative Parameter-Free Clustering of Data with Mixed Type Attributes.- Data Transformation for Sum Squared Residue.- Session 1B. Social Networks.- A Better Strategy of Discovering Link-Pattern Based Communities by Classical Clustering Methods.- Mining Antagonistic Communities from Social Networks.- As Time Goes by: Discovering Eras in Evolving Social Networks.- Online Sampling of High Centrality Individuals in Social Networks.- Estimate on Expectation for Influence Maximization in Social Networks.- Session 1C. Classification I.- A Novel Scalable Multi-class ROC for Effective Visualization and Computation.- Efficiently Finding the Best Parameter for the Emerging Pattern-Based Classifier PCL.- Rough Margin Based Core Vector Machine.- BoostML: An Adaptive Metric Learning for Nearest Neighbor Classification.- A New Emerging Pattern Mining Algorithm and Its Application in Supervised Classification.- Session 2A. Privacy.- Hiding Emerging Patterns with Local Recoding Generalization.- Anonymizing Transaction Data by Integrating Suppression and Generalization.- Satisfying Privacy Requirements: One Step before Anonymization.- Computation of Ratios of Secure Summations in Multi-party Privacy-Preserving Latent Dirichlet Allocation.- Privacy-Preserving Network Aggregation.- Multivariate Equi-width Data Swapping for Private Data Publication.- Session 2B. Spatio-Temporal Mining.- Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets.- Mining Trajectory Corridors Using Fréchet Distance and Meshing Grids.- Subseries Join: A Similarity-Based Time Series Match Approach.- TWave: High-Order Analysis of Spatiotemporal Data.- Spatial Clustering with Obstacles Constraints by Dynamic Piecewise-Mapped and Nonlinear Inertia Weights PSO.- Session 3A. Pattern Mining.- An Efficient GA-Based Algorithm for Mining Negative Sequential Patterns.- Valency Based Weighted Association Rule Mining.- Ranking Sequential Patterns with Respect to Significance.- Mining Association Rules in Long Sequences.- Mining Closed Episodes from Event Sequences Efficiently.- Most Significant Substring Mining Based on Chi-square Measure.- Session 3B. Recommendations/Answers.- Probabilistic User Modeling in the Presence of Drifting Concepts.- Using Association Rules to Solve the Cold-Start Problem in Recommender Systems.- Semi-supervised Tag Recommendation - Using Untagged Resources to Mitigate Cold-Start Problems.- Cost-Sensitive Listwise Ranking Approach.- Mining Wikipedia and Yahoo! Answers for Question Expansion in Opinion QA.- Answer Diversification for Complex Question Answering on the Web.- Vocabulary Filtering for Term Weighting in Archived Question Search.- Session 3C. Topic Modeling/Information Extraction.- On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations.- Supervising Latent Topic Model for Maximum-Margin Text Classification and Regression.- Resource-Bounded Information Extraction: Acquiring Missing Feature Values on Demand.- Efficient Deep Web Crawling Using Reinforcement Learning.- Topic Decomposition and Summarization.- Session 4A. Skylines/Uncertainty.- UNN: A Neural Network for Uncertain Data Classification.- SkyDist: Data Mining on Skyline Objects.- Multi-Source Skyline Queries Processing in Multi-Dimensional Space.- Efficient Pattern Mining of Uncertain Data with Sampling.- Classifier Ensemble for Uncertain Data Stream Classification.